Medical informatics

A Down's Syndrome related Single Minded 2 gene (SIM2), previously known to be
associated with Trisomy 21 was predicted by bioinformatics to be colon cancer specific.
In previous work from the laboratory using a patient tissue repository, an isoform of this
gene, short form (SIM2-s) was shown to be colon cancer specific. Inhibition of SIM2-s
expression by antisense technology resulted in cancer-cell specific apoptosis within 24
hours. Microarray-based gene expression profiling of the antisense-treated colon cancer
cells provided a fingerprint of genes involving key cell cycle, apoptosis, DNA damage
and differentiation genes. Taking hints from the microarray database, experiments were
initiated to decipher the molecular mechanism underlying the cancer specific function of
the SIM2-s gene. Using an isogenic cell system, apoptosis was found to be dependent
on DNA damage and repair gene, GADD45-a. Further, key pathways including p38 MAP
kinase (MAPK) and specific caspases were essential for apoptosis. Programmed cell
death was not dependant on cell cycle and was preceded by the induction of terminal
differentiation. To clarify whether SIM2-s function is a critical determinant of differentiation, stable transfectants of SIM2-s were established in a murine adipocytic
cell line (3T3-L 1 ). SIM2-s overexpression caused a pronounced block of differentiation
of the pre-adipocytes into mature adipocytes. A study of the differentiation pathway in
3T3-L 1 cells suggested that this block occurs early on in the cascade. These results
supported the starting premise that SIM2-s is a critical mediator of cell differentiation. To
clarify whether the SIM2-s gene has transforming potential, the SIM2-s gene was
overexpressed in the NIH3T3 murine fibroblast cell line. The cells expressing the human
SIM2-s gene exhibited shorter doubling time, abrogation of growth serum requirement,
greater cell number at saturation density and focus formation. In vivo tumorigenicity
assays showed tumor formation with long latency. These results provide strong evidence
for the role of SIM2-s gene in tumor cell growth and differentiation, and validate drug
therapy use for the gene.

Mining the human genome for therapeutic target(s) discovery promises novel outcome. Over half of the proteins in the human genome however, remain uncharacterized. These proteins offer a potential for new target(s) discovery for diverse diseases. Additional targets for cancer diagnosis and therapy are urgently needed to help move away from the cytotoxic era to a targeted therapy approach. Bioinformatics and proteomics approaches can be used to characterize novel sequences in the genome database to infer putative function. The hypothesis that the amino acid motifs and proteins domains of the uncharacterized proteins can be used as a starting point to predict putative function of these proteins provided the framework for the research discussed in this dissertation.

Chronic Diseases are the major cause of mortality around the world, accounting for 7 out of 10 deaths each year in the United States. Because of its adverse effect on the quality of life, it has become a major problem globally. Health care costs involved in managing these diseases are also very high. In this thesis, we will focus on two major chronic diseases Asthma and Diabetes, which are among the leading causes of mortality around the globe. It involves design and development of a predictive analytics based decision support system which uses five supervised machine learning algorithm to predict the occurrence of Asthma and Diabetes. This system helps in controlling the disease well in advance by selecting its best indicators and providing necessary feedback.

In this thesis, importance of Intelligent Data Repository (IDR) and its real life applications are studied. We proposed an IDR for oncology applications which can handle large datasets and which can be used on both the intranet and the Internet. It is designed to provide one or multiple medical institutions on a global scale a common platform for patient care and consultation. The proposed application consists of two key models, Body Surface Area model and Search model, which are described in detail and their results are discussed. We have implemented the proposed IDR for oncology application using ColdFusion MX. Existing research in this area have been studied and compared. Framework of the proposed IDR, structure, front-end user interface and back-end database schema of the proposed oncology application are explained in this thesis.

Medical information is very private and sensitive. With the digitization of medical data, it is becoming accessible through distributed systems, including the Internet. Access to all this information and appropriate exchange of data makes the job of health providers more effective, however, the number of people that can potentially access this information increases by orders of magnitude. Private health information is not well protected. We present guidelines for security models for medical information systems. First, we model the structure of the medical information in the form of object-oriented patterns. Second, we study models and patterns in use today and compare them to our patterns. Next we define requirements necessary for controlling access, and describe the common policies and restrictions of security models for medical applications. We present some of the medical record access control restrictions directly in a conceptual model of the medical information.

Harnessing the human genome using bioinformatics lead to the discovery of a highly cancer-selective gene, Single Minded 2 gene (SIM2). An isoform of the SIM2 gene, the short-form (SIM2-s), was shown to be specific to colon, pancreas, and prostate tumors. Antisense inhibition of SIM2-s in a colon carcinoma derived cell line (RKO) caused inhibition of gene expression, growth inhibition and apoptosis in vitro and in nude mice tumorigenicity models. To understand the mechanism of Sim2-s antisense, the antisense treated RKO colon cancer cells were monitored for genome wide expression using Affymetrix GeneChipRTM technology. A list of apoptosis related genes was generated using GeneSpringRTM software. Select GeneChip RTM output was validated by Quantitative RT-PCR. Relevance of a key gene, Growth arrest and DNA damage inducible (GADD45a), in the SIM2-s pathway was established. These results will provide a basis for the future experiments to understand the mechanism underlying Sim2-s activation in specific tumors.

Though several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ; they require long battery life, and ultimately fall short on delivering real-time continuous detection of arrhythmia and specifically progressive development of cardiac A-Fib. The focus of this dissertation is the design of a class of adaptive and efficient energy-aware real-time detection models for monitoring, early real-time detection and reporting of progressive development of cardiac A-Fib.... The design promises to have a greater positive public health impact from predicting A-Fib and providing a viable approach to meeting the energy needs of current and future real-time monitoring, detecting and reporting required in wearable computing healthcare applications that are constrained by scarce energy resources.

The efforts addressed in this thesis refer to applying nonlinear risk predictive techniques based on logistic regression to medical diagnostic test data. This study is motivated and pursued to address the following: 1. To extend logistic regression model of biostatistics to medical informatics 2. Computational preemptive and predictive testing to determine the probability of occurrence (p) of an event by fitting a data set to a (logit function) logistic curve: Finding upper and lower bounds on p based on stochastical considerations 3. Using the model developed on available (clinical) data to illustrate the bounds-limited performance of the prediction. Relevant analytical methods, computational efforts and simulated results are presented. Using the results compiled, the risk evaluation in medical diagnostics is discussed with real-world examples. Conclusions are enumerated and inferences are made with directions for future studies.

The use of wireless sensor networks for a myriad of applications is increasing. They can be used in healthcare for emergency management. In Florida, hurricanes are the main source of natural disasters. There has been a high incidence of hurricanes over the past decade. When a hurricane warning is issued it is important that people who live in potentially dangerous areas, such as along the coast, evacuate for their safety. Nursing homes and other care facilities for elderly or disabled people experience difficulty with the evacuation as their residents require additional assistance. The characteristics and challenges of a hurricane evacuation are investigated. A patient-centric hurricane evacuation management system is proposed to allow healthcare providers the ability to continuously monitor and track patients. During a hurricane there are usually scarce energy resources and a loss of basic communication services such as cellular service and Internet access. We propose the architecture of the system that allows it to operate in the absence of these services. The hardware and software architectures are also presented along with the main phases of operation. The system was then validated and the performance evaluated via simulation using the OPNET Modeler.

With the increasing demands of rising medical costs in combination with a boom in elderly patients in need of quality patient care medical practices are being stressed. Patient to nurse ratios are increasing and government spending in the medical domain is at an all-time high threatening the futures of government medical programs such as Medicare and Medicaid. In this thesis we propose a framework for the monitoring of a patient's vital statistics in a home-based setting using a mobile smart device. We believe that in taking advantage of the wireless sensor technology which is readily available today we can provide a solution that is both economically and socially viable offering a solid quality of healthcare in a comfortable and familiar environment. Our framework exposes both 802.11 and Bluetooth wireless protocol transmitting medical sensor devices using an Android platform device as a monitoring hub.